Analysis of no-wait flow shop scheduling problems and solving with hybrid scatter search method

Beklemesiz Akış Tipi Çizelgeleme (BATÇ), pratik uygulamalarından dolayı kapsamlı bir araştırma alanıdır. BATÇ problemlerinde işler, makinelerde kesintisiz olarak işlem görmek zorundadır. Bir işin tüm makinelerde işlenme süresi boyunca, makineler bekleyebilir fakat işler kesintisiz olarak işlenmelidir. Amaç ise makinelerin boşta bekleme süresini en aza indirmektir. BATÇ problemlerinin çoğunluğunda toplam gecikmenin ve maksimum tamamlanma zamanının minimizasyonu olmak üzere, iki performans ölçüsü göz önünde bulundurulur. Literatürde, son yirmi beş yılda BATÇ ile ilgili yapılan çalışmalar analiz edilmiştir. BATÇ problemlerinin çözümü ile ilgili geliştirilen kesin ve yaklaşık çözüm veren yöntemler incelenmiştir. Literatürde 1 ve 2 makineli problemler için optimum çözüm veren matematiksel yöntemler bulunurken, 3 ve daha fazla makineli problemler için standart zamanda optimum çözüm veren bir yöntem bulunmamaktadır. Kabul edilebilir bir süre içerisinde m makine içeren problemlere optimum ya da optimuma yakın çözümler üretebilmek için sezgisel ve meta sezgisel yöntemler geliştirilmektedir. Bu çalışmada, BATÇ problemlerinin çözümü için Hibrit Dağınık Arama (HDA) yöntemi önerilmiştir. Önerilen yöntem, literatürde iyi bilinen kıyaslama problemleri yardımı ile test edilmiştir. Elde edilen sonuçlar, Hibrit Uyarlanabilir Öğrenme Yaklaşım (HUÖY) algoritması ve Hibrit Karınca Kolonileri Optimizasyon (HKKO) algoritması ile kıyaslanmıştır. Amaç fonksiyonu olarak maksimum tamamlanma zamanının minimizasyonu seçilmiştir. Elde edilen çözüm sonuçları, önerilen HDA yönteminin BATÇ problemlerinin çözümünde etkili olduğunu göstermiştir.No-wait flow shop (NWFS) is extensively research area due to its practical applications. In NWFS, jobs are processed in machines without interruption. During the schedule period, machines can wait, but jobs cannot wait. The aim is to minimize the idle time for machines. The majority of NWFS, two performance measures are consid-ered: minimization of total delay and minimization of the makespan. The researches on the NWFS in the last twenty-five years have been analysed from the literature. The methods developed for the solution of the NWFS, which give exact and approximate solutions, have been examined. While there are mathematical methods that give optimum solutions for 1 and 2 machine problems in the literature, there is no method that provides optimum solutions in standard time for problems with 3 or more machines. The difference methods are developed in order to produce optimum or near-optimum solutions to m-machine problems in an acceptable time. A Hybrid Scatter Search Method (HSSM) is proposed for solving the NWFS. The developed HSSM tested with the well-known benchmarking instances in the literature. The results obtained were compared with the Hybrid Adaptive Learning Approach algorithm and the Hybrid Ant Colonies Optimization algorithm. The objective function is makespan minimization. According to solutions, the proposed HSSM is an effective metaheuristic to solve NWFS

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

The Sixth Annual Workshop on Space Operations Applications and Research (SOAR 1992)

This document contains papers presented at the Space Operations, Applications, and Research Symposium (SOAR) hosted by the U.S. Air Force (USAF) on 4-6 Aug. 1992 and held at the JSC Gilruth Recreation Center. The symposium was cosponsored by the Air Force Material Command and by NASA/JSC. Key technical areas covered during the symposium were robotic and telepresence, automation and intelligent systems, human factors, life sciences, and space maintenance and servicing. The SOAR differed from most other conferences in that it was concerned with Government-sponsored research and development relevant to aerospace operations. The symposium's proceedings include papers covering various disciplines presented by experts from NASA, the USAF, universities, and industry

Integrating Recognition and Decision Making to Close the Interaction Loop for Autonomous Systems

Intelligent systems are becoming increasingly ubiquitous in daily life. Mobile devices are providing machine-generated support to users, robots are coming out of their cages in manufacturing to interact with co-workers, and cars with various degrees of self-driving capabilities operate amongst pedestrians and the driver. However, these interactive intelligent systems\u27 effectiveness depends on their understanding and recognition of human activities and goals, as well as their responses to people in a timely manner. The average person does not follow instructions step-by-step or act in a formulaic manner, but instead varies the order of actions and timing when performing a given task. People explore their surroundings, make mistakes, and may interrupt an activity to handle more urgent matters. The decisions that an autonomous intelligent system makes should account for such noise and variance regardless of the form of interaction, which includes adapting action choices and possibly its own goals.While most people take these aspects of interaction for granted, they are complex and involve many specific tasks that have primarily been studied independently within artificial intelligence. This results in open-loop interactive experiences where the user must perform a fixed input command or the intelligent system performs a hard-coded output response---one of the components of the interaction cannot adapt with respect to the other for longer-term back-and-forth interactions. This dissertation explores how developments in plan recognition, activity recognition, intent recognition, and autonomous planning can work together to develop more adaptive interactive experiences between autonomous intelligent systems and the people around them. In particular, we consider a unifying perspective of recognition algorithms that provides sufficient information to dynamically produce short-term automated planning problems, and we present ways to run these algorithms faster for the real-time needs of interaction. This exploration leads to the introduction of the Planning and Recognition Together Close the Interaction Loop (PReTCIL) framework that serves as a first step towards identifying how we can address the problem of closing the interaction loop, in addition to new questions that need to be considered

Foundations of Human-Aware Planning -- A Tale of Three Models

abstract: A critical challenge in the design of AI systems that operate with humans in the loop is to be able to model the intentions and capabilities of the humans, as well as their beliefs and expectations of the AI system itself. This allows the AI system to be "human- aware" -- i.e. the human task model enables it to envisage desired roles of the human in joint action, while the human mental model allows it to anticipate how its own actions are perceived from the point of view of the human. In my research, I explore how these concepts of human-awareness manifest themselves in the scope of planning or sequential decision making with humans in the loop. To this end, I will show (1) how the AI agent can leverage the human task model to generate symbiotic behavior; and (2) how the introduction of the human mental model in the deliberative process of the AI agent allows it to generate explanations for a plan or resort to explicable plans when explanations are not desired. The latter is in addition to traditional notions of human-aware planning which typically use the human task model alone and thus enables a new suite of capabilities of a human-aware AI agent. Finally, I will explore how the AI agent can leverage emerging mixed-reality interfaces to realize effective channels of communication with the human in the loop.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios